Teleradiology and Artificial Intelligence in the Diagnosis of Musculoskeletal Diseases: Practical Recommendations

Musculoskeletal disorders represent one of the most prevalent chronic disease groups worldwide. Inflammatory rheumatic diseases, particularly rheumatoid arthritis and spondyloarthritis, require early detection of structural joint damage and consistent monitoring of treatment response. Teleradiology addresses geographical limitations by enabling the remote transmission and interpretation of radiological images via digital networks, thereby improving access to urgent care and subspecialty expertise. Artificial intelligence, driven by advances in image recognition and pattern analysis, is increasingly incorporated into radiological workflows to assist clinicians with diagnostic tasks such as fracture detection, identification of sacroiliac joint inflammation, and scoring of erosion and synovitis in hand magnetic resonance imaging. This article evaluates the contributions of these technologies to the diagnosis and monitoring of musculoskeletal disorders based on current evidence. Key findings indicate that various platforms have demonstrated improvements in diagnostic performance, that deep learning models show promise in spondyloarthritis imaging, and that asynchronous telemedicine models have the potential to reduce diagnostic delays. Nevertheless, the absence of external validation, challenges related to explainability, and a lack of clinical trials specifically addressing teleradiology remain significant barriers. Sustainable implementation of these technologies in musculoskeletal clinics requires an integrated approach encompassing robust technical infrastructure, comprehensive staff training, and multidisciplinary monitoring mechanisms.